The present invention relates generally to property valuation and more particularly to automated property valuation.
Property valuation is a process of determining a dollar estimate of a property""s value for given market conditions. The value of a property changes with market conditions. Consequently, a property""s value is often updated to reflect changes in market conditions, including for example, recent real estate transactions.
Property valuations have many applications. For example, many financial institutions grant new mortgages to homebuyers, and purchase mortgage packages, which can contain hundreds of mortgages, on the secondary market as investments. Property valuations are usually necessary to grant most new mortgages, as well as to evaluate mortgage packages that may be available for purchased. By way of further example, property valuations are also used to guide buyers and sellers with making purchasing decisions, and are needed for a variety of insurance purposes.
The current process for valuing properties usually requires an on-site visit by a human appraiser, can take several days, and cost hundreds of dollars per subject property. The process usually used by appraisers is a sales comparison approach, which consists of finding comparables (i.e., recent sales that are comparable to the subject property, using for example sales records), contrasting the subject property with the comparables, adjusting the comparables"" sales price to reflect the differences from the subject property, using for example, heuristics and personal experience, and reconciling the comparables"" adjusted sales prices to derive an estimate for the subject property, using any reasonable averaging method.
The human appraisal process is slow and expensive for multiple appraisals, which are often required by banks to, for example, update their loan and insurance portfolios, verify risk profiles of servicing rights, or evaluate default risks for securitized mortgage packages. Consequently, the appraisal process for multiple valuations is currently estimated, to a lesser degree of accuracy, by sampling techniques.
Thus, there is a particular need to automate the valuation process. The present invention is a method and system for automating the valuation process that produces an estimated value of a subject property that is based on a generative artificial intelligence method that trains a fuzzy-neural network using a subset of cases from a case-base, and produces a run-time system to provide an estimate of the subject property""s value.
In one embodiment, the system is a network-based implementation of fuzzy inference based on a system that implements a fuzzy system as a 5-layer neural network so that the structure of the network can be interpreted in terms of high-level rules. The neural network is trained automatically from data. IF/THEN rules are used to map inputs to outputs by a fuzzy logic inference system. Different models for the same problem can be obtained by changing the inputs to the neuro-fuzzy network, or by varying the network""s architecture.